<p>
  In this tutorial we will take a close look at a principal component analysis (PCA)-based statistical arbitrage strategy
  derived from the paper <a target=_BLANK href="https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf">
  Statistical Arbitrage in the U.S. Equities Market</a>.
</p>
<p>
  Statistical arbitrage strategies uses mean-reversion models to take advantage of pricing inefficiencies between groups of correlated 
  securities. This class of short-term financial trading strategies produce moves that can contrarian to the broader market movement and are often discussed in conjunction with 
  <a traget="_BLANK" href="https://www.quantconnect.com/tutorials/strategy-library/pairs-trading-with-stocks">Pairs Trading</a>. 
  In our algorithm, we will be using a PCA-based approach as opposed to an ETF-based approach to limit our universe of stocks. 
  Backtests from the period 1997-2007 support our strategy by showing that PCA-based strategies have Sharpe ratios that outperform Sharpe ratios 
  from ETF-based strategies. 
</p>

